Although existing face anti-spoofing (FAS) methods achieve high accuracy in
intra-domain experiments, their effects drop severely in cross-domain scenarios
because of poor generalization. Recently, multifarious techniques have been
explored, such as domain generalization and representation disentanglement.
However, the improvement is still limited by two issues: 1) It is difficult to
perfectly map all faces to a shared feature space. If faces from unknown
domains are not mapped to the known region in the shared feature space,
accidentally inaccurate predictions will be obtained. 2) It is hard to
completely consider various spoof traces for disentanglement. In this paper, we
propose a Feature Generation and Hypothesis Verification framework to alleviate
the two issues. Above all, feature generation networks which generate
hypotheses of real faces and known attacks are introduced for the first time in
the FAS task. Subsequently, two hypothesis verification modules are applied to
judge whether the input face comes from the real-face space and the real-face
distribution respectively. Furthermore, some analyses of the relationship
between our framework and Bayesian uncertainty estimation are given, which
provides theoretical support for reliable defense in unknown domains.
Experimental results show our framework achieves promising results and
outperforms the state-of-the-art approaches on extensive public datasets.

本文提出了一种特征生成和假设验证框架来缓解领域泛化和表示解缠问题，该框架引入了生成真实人脸和已知攻击假设的特征生成网络，进而应用两个假设验证模块来判断输入人脸是否来自真人脸空间和真人脸分布，实验结果表明我们的方法取得了有前途的结果，超过了广泛公开数据集上最先进的方法。